A Multiple Classifier Learning by Sampling System for White Blood Cells Segmentation.

The visual analysis and the counting of white blood cells in microscopic peripheral blood smears is a very important procedure in the medical field. It can provide useful information concerning the health of the patients, e.g., the diagnosis of Acute Lymphatic Leukaemia or other important diseases. Blood experts in clinical centres traditionally use these methods in order to perform a manual analysis. The main issues of the traditional human analysis are certainly related to the difficulties encountered during this type of procedure: generally, the process is not rapid and it is strongly influenced by the operator's capabilities and tiredness. The main purpose of this work is to realize a reliable automated multiple classifier system based on Nearest Neighbour and Support Vector Machine in order to manage all the regions of immediate interests inside a blood smear: white blood cells nucleus and cytoplasm, erythrocytes and background. The experimental results demonstrate that the proposed method is very accurate and robust being able to reach an accuracy in segmentation of 99%, indicating the possibility to tune this approach to each couple of microscope and camera.